import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn import metrics

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

df= pd.read_csv('vehicle.csv')

df.head(10)

df.shape

(846, 19)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 846 entries, 0 to 845
Data columns (total 19 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   compactness                  846 non-null    int64  
 1   circularity                  841 non-null    float64
 2   distance_circularity         842 non-null    float64
 3   radius_ratio                 840 non-null    float64
 4   pr.axis_aspect_ratio         844 non-null    float64
 5   max.length_aspect_ratio      846 non-null    int64  
 6   scatter_ratio                845 non-null    float64
 7   elongatedness                845 non-null    float64
 8   pr.axis_rectangularity       843 non-null    float64
 9   max.length_rectangularity    846 non-null    int64  
 10  scaled_variance              843 non-null    float64
 11  scaled_variance.1            844 non-null    float64
 12  scaled_radius_of_gyration    844 non-null    float64
 13  scaled_radius_of_gyration.1  842 non-null    float64
 14  skewness_about               840 non-null    float64
 15  skewness_about.1             845 non-null    float64
 16  skewness_about.2             845 non-null    float64
 17  hollows_ratio                846 non-null    int64  
 18  class                        846 non-null    object 
dtypes: float64(14), int64(4), object(1)
memory usage: 125.7+ KB

df.isnull().sum()

compactness                    0
circularity                    5
distance_circularity           4
radius_ratio                   6
pr.axis_aspect_ratio           2
max.length_aspect_ratio        0
scatter_ratio                  1
elongatedness                  1
pr.axis_rectangularity         3
max.length_rectangularity      0
scaled_variance                3
scaled_variance.1              2
scaled_radius_of_gyration      2
scaled_radius_of_gyration.1    4
skewness_about                 6
skewness_about.1               1
skewness_about.2               1
hollows_ratio                  0
class                          0
dtype: int64

df.describe().T

df_nonnumeric= df.iloc[:,-1]
df_numeric= df.iloc[:,:-1]

df_numeric.head()

df_numeric.median()

compactness                     93.0
circularity                     44.0
distance_circularity            80.0
radius_ratio                   167.0
pr.axis_aspect_ratio            61.0
max.length_aspect_ratio          8.0
scatter_ratio                  157.0
elongatedness                   43.0
pr.axis_rectangularity          20.0
max.length_rectangularity      146.0
scaled_variance                179.0
scaled_variance.1              363.5
scaled_radius_of_gyration      173.5
scaled_radius_of_gyration.1     71.5
skewness_about                   6.0
skewness_about.1                11.0
skewness_about.2               188.0
hollows_ratio                  197.0
dtype: float64

column = df_numeric.columns

length = len(column)

for i in range(0,length):
    df_numeric[column[i]].fillna(df_numeric[column[i]].median(), inplace = True)

df.skew()

compactness                    0.381271
circularity                    0.261809
distance_circularity           0.106585
radius_ratio                   0.394978
pr.axis_aspect_ratio           3.830362
max.length_aspect_ratio        6.778394
scatter_ratio                  0.607271
elongatedness                  0.047847
pr.axis_rectangularity         0.770889
max.length_rectangularity      0.256359
scaled_variance                0.651598
scaled_variance.1              0.842034
scaled_radius_of_gyration      0.279317
scaled_radius_of_gyration.1    2.083496
skewness_about                 0.776519
skewness_about.1               0.688017
skewness_about.2               0.249321
hollows_ratio                 -0.226341
dtype: float64

from sklearn.preprocessing import PowerTransformer
pt = PowerTransformer()

pt.fit(df_numeric)
orig_df = pd.DataFrame(pt.fit_transform(df_numeric))
orig_df.columns = df_numeric.columns
orig_df.head()

orig_df.skew()

compactness                    0.020835
circularity                    0.009902
distance_circularity          -0.029340
radius_ratio                  -0.006298
pr.axis_aspect_ratio          -0.099316
max.length_aspect_ratio       -0.183269
scatter_ratio                  0.049224
elongatedness                 -0.043290
pr.axis_rectangularity         0.124225
max.length_rectangularity      0.011789
scaled_variance                0.024486
scaled_variance.1              0.046460
scaled_radius_of_gyration     -0.009842
scaled_radius_of_gyration.1    0.001559
skewness_about                -0.086317
skewness_about.1              -0.096120
skewness_about.2               0.019008
hollows_ratio                 -0.040011
dtype: float64

df= pd.concat([orig_df,df_nonnumeric], axis=1)

df.head()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 846 entries, 0 to 845
Data columns (total 19 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   compactness                  846 non-null    float64
 1   circularity                  846 non-null    float64
 2   distance_circularity         846 non-null    float64
 3   radius_ratio                 846 non-null    float64
 4   pr.axis_aspect_ratio         846 non-null    float64
 5   max.length_aspect_ratio      846 non-null    float64
 6   scatter_ratio                846 non-null    float64
 7   elongatedness                846 non-null    float64
 8   pr.axis_rectangularity       846 non-null    float64
 9   max.length_rectangularity    846 non-null    float64
 10  scaled_variance              846 non-null    float64
 11  scaled_variance.1            846 non-null    float64
 12  scaled_radius_of_gyration    846 non-null    float64
 13  scaled_radius_of_gyration.1  846 non-null    float64
 14  skewness_about               846 non-null    float64
 15  skewness_about.1             846 non-null    float64
 16  skewness_about.2             846 non-null    float64
 17  hollows_ratio                846 non-null    float64
 18  class                        846 non-null    object 
dtypes: float64(18), object(1)
memory usage: 125.7+ KB

sns.pairplot(df, hue = 'class')

<seaborn.axisgrid.PairGrid at 0x1619037d6a0>

df.corr()

clf=svm.SVC()

X=df.iloc[:,:-1]
y= df.iloc[:,-1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)

clf.fit(X_train, y_train)

SVC()

clf.score(X_test, y_test)

0.9566929133858267

y_pred = clf.predict(X_test)
accuracy_score(y_test, y_pred)

0.9566929133858267

pd.crosstab(y_test, y_pred)

print(metrics.classification_report(y_test, y_pred))

              precision    recall  f1-score   support

         bus       0.92      1.00      0.96        59
         car       0.98      0.96      0.97       133
         van       0.93      0.90      0.92        62

    accuracy                           0.96       254
   macro avg       0.95      0.96      0.95       254
weighted avg       0.96      0.96      0.96       254

X1=df.iloc[:,:-1]
y1= df.iloc[:,-1]

sc = StandardScaler()
X_std =  sc.fit_transform(X1)          
cov_matrix = np.cov(X_std.T)
print('Covariance Matrix \n%s', cov_matrix)

Covariance Matrix 
%s [[ 1.00118343  0.64928071  0.77858213  0.70880729  0.16177611  0.35944219
   0.77251518 -0.78547172  0.77734376  0.65530004  0.7427099   0.77614515
   0.54429887 -0.27143577  0.19731709  0.14389942  0.36453607  0.39064704]
 [ 0.64928071  1.00118343  0.78137544  0.61483087  0.1893121   0.45900331
   0.82882941 -0.81708621  0.82949691  0.96358033  0.79364869  0.82259206
   0.93337754  0.1052041   0.13835956 -0.0306774  -0.0787538   0.02943942]
 [ 0.77858213  0.78137544  1.00118343  0.78719134  0.23363637  0.55504022
   0.91561086 -0.91274554  0.91268863  0.76768225  0.88520049  0.91476224
   0.69134771 -0.20553513  0.08460481  0.24150986  0.19305789  0.32456351]
 [ 0.70880729  0.61483087  0.78719134  1.00118343  0.68660665  0.48608324
   0.77857472 -0.80548087  0.76270496  0.56835048  0.8213446   0.79101898
   0.52628834 -0.29375624  0.03179368  0.1581088   0.45019476  0.49295344]
 [ 0.16177611  0.1893121   0.23363637  0.68660665  1.00118343  0.30851409
   0.20796094 -0.24199761  0.18945636  0.14490169  0.27584132  0.22536673
   0.1337144  -0.19655254 -0.0517439  -0.02649184  0.39326274  0.40409996]
 [ 0.35944219  0.45900331  0.55504022  0.48608324  0.30851409  1.00118343
   0.41378358 -0.39072676  0.41649714  0.53909765  0.43137878  0.40159375
   0.32684376 -0.1973776   0.02638484  0.11216958  0.11255057  0.35824721]
 [ 0.77251518  0.82882941  0.91561086  0.77857472  0.20796094  0.41378358
   1.00118343 -0.99466158  0.98763363  0.79258191  0.9609034   0.99592191
   0.77437624 -0.01742101  0.04752839  0.1894784   0.07199898  0.15175228]
 [-0.78547172 -0.81708621 -0.91274554 -0.80548087 -0.24199761 -0.39072676
  -0.99466158  1.00118343 -0.98267372 -0.77437648 -0.96461898 -0.99745031
  -0.76115096  0.05571512 -0.04645186 -0.17269111 -0.13471998 -0.19535697]
 [ 0.77734376  0.82949691  0.91268863  0.76270496  0.18945636  0.41649714
   0.98763363 -0.98267372  1.00118343  0.7990059   0.94516304  0.98539989
   0.77293273 -0.01311419  0.05424988  0.19260506  0.05587928  0.1425479 ]
 [ 0.65530004  0.96358033  0.76768225  0.56835048  0.14490169  0.53909765
   0.79258191 -0.77437648  0.7990059   1.00118343  0.74897684  0.78360226
   0.87288868  0.08388954  0.12786409 -0.01088969 -0.07091129  0.06503559]
 [ 0.7427099   0.79364869  0.88520049  0.8213446   0.27584132  0.43137878
   0.9609034  -0.96461898  0.94516304  0.74897684  1.00118343  0.96340814
   0.75743093  0.04582379  0.01618583  0.176849    0.0862275   0.12630146]
 [ 0.77614515  0.82259206  0.91476224  0.79101898  0.22536673  0.40159375
   0.99592191 -0.99745031  0.98539989  0.78360226  0.96340814  1.00118343
   0.76771974 -0.03423153  0.04559762  0.17837992  0.10310782  0.17315527]
 [ 0.54429887  0.93337754  0.69134771  0.52628834  0.1337144   0.32684376
   0.77437624 -0.76115096  0.77293273  0.87288868  0.75743093  0.76771974
   1.00118343  0.25480132  0.17137877 -0.06989    -0.21060282 -0.13615104]
 [-0.27143577  0.1052041  -0.20553513 -0.29375624 -0.19655254 -0.1973776
  -0.01742101  0.05571512 -0.01311419  0.08388954  0.04582379 -0.03423153
   0.25480132  1.00118343 -0.07850087 -0.10112969 -0.84769285 -0.89037257]
 [ 0.19731709  0.13835956  0.08460481  0.03179368 -0.0517439   0.02638484
   0.04752839 -0.04645186  0.05424988  0.12786409  0.01618583  0.04559762
   0.17137877 -0.07850087  1.00118343 -0.03261896  0.09784631  0.07008885]
 [ 0.14389942 -0.0306774   0.24150986  0.1581088  -0.02649184  0.11216958
   0.1894784  -0.17269111  0.19260506 -0.01088969  0.176849    0.17837992
  -0.06989    -0.10112969 -0.03261896  1.00118343  0.06929987  0.17620544]
 [ 0.36453607 -0.0787538   0.19305789  0.45019476  0.39326274  0.11255057
   0.07199898 -0.13471998  0.05587928 -0.07091129  0.0862275   0.10310782
  -0.21060282 -0.84769285  0.09784631  0.06929987  1.00118343  0.90235828]
 [ 0.39064704  0.02943942  0.32456351  0.49295344  0.40409996  0.35824721
   0.15175228 -0.19535697  0.1425479   0.06503559  0.12630146  0.17315527
  -0.13615104 -0.89037257  0.07008885  0.17620544  0.90235828  1.00118343]]

eigenvalues, eigenvectors = np.linalg.eig(cov_matrix)
print('Eigen Vectors \n%s', eigenvectors)
print('\n Eigen Values \n%s', eigenvalues)

Eigen Vectors 
%s [[-2.65249806e-01 -1.03022012e-01  2.41878917e-01 -1.68212796e-02
  -1.48092118e-01 -5.94202136e-02  1.61229043e-01 -8.27249133e-01
  -2.58635515e-01 -1.11120054e-01 -1.43186585e-01 -8.28627981e-02
  -7.26179018e-02 -9.62242849e-02 -4.81204894e-02  3.01778484e-02
  -4.75830215e-03  7.25499175e-03]
 [-2.83739997e-01  1.55767519e-01 -5.11666331e-03 -2.10371203e-01
   8.27583413e-02 -1.03537138e-01 -4.09070451e-01 -2.66692189e-02
  -8.33878765e-03  7.15939886e-02  4.64365455e-02  2.00194517e-01
   1.07243361e-01 -5.16262686e-01  5.72840539e-01  1.03747196e-01
  -2.91190097e-02 -1.05685825e-02]
 [-3.04039660e-01 -3.62756689e-02  7.30756307e-02  9.11467591e-02
   8.15573542e-02 -7.43393903e-02  1.15297741e-01  1.83306358e-01
  -3.74158779e-01 -2.76833875e-01  7.50965614e-01 -1.67097117e-01
  -1.49596638e-01  1.89889841e-02  3.61053590e-02 -2.78687230e-02
   8.49468526e-03 -1.13446286e-02]
 [-2.74143941e-01 -1.88579602e-01 -2.50161489e-01  2.92723167e-03
  -8.66319967e-02  2.49671105e-01  9.85828727e-02 -4.62828791e-02
  -2.13286630e-02 -2.35389193e-01 -1.87181840e-02  2.81435979e-01
   5.71069770e-01  3.33222855e-01  2.28797987e-01 -3.45706449e-01
  -2.56958203e-03  2.33720809e-02]
 [-1.04195716e-01 -2.35493808e-01 -6.21822552e-01 -1.71347341e-01
   6.14117485e-03  5.09172018e-01 -1.28469587e-01 -5.57417052e-02
  -2.56502633e-01  1.61656982e-01 -1.44759084e-02 -7.94355783e-02
  -2.78218773e-01 -1.29023261e-01 -1.47161022e-01  1.56419453e-01
  -3.57653719e-03 -1.10569840e-02]
 [-1.71617997e-01 -1.00205131e-01 -1.97812492e-01 -1.07865539e-01
   7.77470493e-01 -2.22541002e-01  3.95770363e-01 -3.60789181e-02
   1.56742463e-01 -7.58563548e-02 -1.79499328e-01  3.45267306e-02
  -1.59925512e-01 -1.16669863e-02  7.31628466e-02 -5.11752360e-02
   1.35208987e-02  1.48123343e-02]
 [-3.11894600e-01  6.25928350e-02  3.77814890e-02  1.17105313e-01
  -1.11904043e-01 -2.12654014e-03  1.17360308e-01  1.58479902e-01
  -5.98079998e-04  1.99553125e-01 -1.53314918e-01 -5.47931719e-02
  -7.96429394e-02 -1.14694428e-01 -7.82110802e-02 -2.90108604e-01
  -2.68058316e-01 -7.64384611e-01]
 [ 3.12501845e-01 -3.49218209e-02 -2.80012452e-02 -1.04918257e-01
   1.64255501e-01 -8.70690555e-03 -1.05623879e-01 -1.53526772e-01
  -6.71882382e-02 -1.65717947e-01  1.24704245e-01  5.60312141e-02
   1.07867560e-01  1.61356773e-01  4.40789132e-02  2.48698472e-01
  -8.09360912e-01 -1.42656483e-01]
 [-3.10107352e-01  6.86945400e-02  5.36072098e-02  1.15259740e-01
  -9.51299321e-02 -1.33933958e-02  1.12706693e-01  1.23631472e-01
  -8.73177256e-02  2.57149600e-01 -1.81787124e-01 -9.05204800e-02
  -1.09428357e-01  5.27499115e-01  3.78346077e-01  5.45288179e-01
   1.24450218e-03  5.66774283e-03]
 [-2.75756169e-01  1.41079995e-01  1.77903514e-02 -2.06055603e-01
   1.98091375e-01 -1.89854559e-01 -3.68938048e-01 -1.75738029e-01
   4.82272218e-02  4.30702886e-01  2.41037351e-01  2.27439863e-01
   9.98227668e-02  3.36621138e-01 -4.38728528e-01 -6.55216577e-02
   3.12646668e-02  1.74664602e-02]
 [-3.05662231e-01  6.13597246e-02 -5.41714358e-02  1.19085663e-01
  -1.11925478e-01  7.19007891e-02  2.06212530e-01  5.83492139e-02
   3.73128658e-01 -2.30475022e-01  8.53938270e-02  2.29644351e-01
   2.22819069e-01 -2.75225859e-01 -3.88118080e-01  5.40606731e-01
  -2.58420584e-02 -2.71164327e-02]
 [-3.12154429e-01  4.94496819e-02  3.08405978e-02  1.10519172e-01
  -1.39948022e-01  2.23898254e-03  1.13704640e-01  1.64553402e-01
   4.51769257e-02  1.93787554e-01 -1.15036192e-01 -7.79965221e-02
  -1.09003427e-01 -1.41164556e-01 -7.79128557e-02 -2.67335789e-01
  -5.18284330e-01  6.24878921e-01]
 [-2.58464283e-01  2.35717337e-01  6.86194888e-05 -2.21617995e-01
   2.31352459e-03  5.93323082e-03 -4.13924835e-01  1.09529528e-01
   9.13331285e-02 -6.04624532e-01 -3.06478343e-01 -2.93737636e-01
  -1.71419057e-01  2.00037262e-01 -1.37304989e-01 -5.23628433e-02
  -5.07417276e-03 -2.22617488e-03]
 [ 3.59699918e-02  4.95044359e-01 -1.89384724e-01  4.70017592e-02
   1.55667117e-02  2.76022427e-01  1.26219421e-01 -3.21426244e-01
   4.57390825e-01  1.03285531e-01  3.03376996e-01 -3.93194435e-01
   2.01133538e-02  5.84681415e-02  1.95309208e-01 -1.04337837e-01
  -5.10454494e-03 -2.21290869e-02]
 [-3.27733460e-02 -2.16280242e-02  5.81746175e-01 -4.93016639e-01
   1.69689325e-01  5.76471747e-01  1.71064648e-01  1.36688228e-01
   1.22038034e-02  7.47046586e-02  1.37513605e-02  1.87030018e-02
   3.29445546e-02 -1.29467737e-02 -1.46432022e-02  1.21956086e-02
   3.46209019e-03  2.54272115e-03]
 [-5.16845687e-02 -8.90837542e-02  2.35988151e-01  7.04782145e-01
   3.86938950e-01  3.74619186e-01 -3.59021783e-01 -8.17792526e-02
   2.85061791e-02 -2.44150397e-02 -5.30695583e-02  6.65788615e-02
  -4.79393685e-02 -9.89681605e-03 -1.84352801e-03 -1.03510969e-02
   6.63531149e-03  1.14113489e-02]
 [-5.80127645e-02 -5.08765049e-01  7.44552697e-02 -7.99226140e-02
  -2.19311200e-01 -2.89307619e-02 -1.09060928e-01 -8.17955791e-02
   5.50010905e-01 -5.82258827e-02  2.01869673e-01  1.99287806e-01
  -4.62331800e-01  1.33204122e-01  1.73797165e-01 -8.29881440e-02
  -3.95143520e-02 -4.27211035e-02]
 [-8.94857243e-02 -5.08821208e-01  5.42024985e-02 -4.23570035e-02
   7.61489140e-02 -1.33819526e-01 -1.54907594e-01  4.56562502e-02
   1.59606312e-01  1.52320089e-01  1.26861687e-02 -6.54339122e-01
   4.28220630e-01 -9.02972146e-02 -3.25694781e-02  9.38552664e-02
   1.96927259e-04 -1.34815346e-02]]

 Eigen Values 
%s [9.64104985e+00 3.29869627e+00 1.17183871e+00 1.19739923e+00
 8.59916998e-01 7.84115472e-01 3.72629538e-01 2.56895019e-01
 1.26797293e-01 9.18091536e-02 6.84368623e-02 5.46198698e-02
 3.70337677e-02 2.07847621e-02 1.88231087e-02 1.32661767e-02
 2.77910098e-03 4.41059005e-03]

# Make a set of (eigenvalue, eigenvector) pairs
eig_pairs = [(eigenvalues[index], eigenvectors[:,index]) for index in range(len(eigenvalues))]


eig_pairs.sort()

eig_pairs.reverse()
print(eig_pairs)


eigvalues_sorted = [eig_pairs[index][0] for index in range(len(eigenvalues))]
eigvectors_sorted = [eig_pairs[index][1] for index in range(len(eigenvalues))]


print('Eigenvalues in descending order: \n%s' %eigvalues_sorted)

[(9.64104985312631, array([-0.26524981, -0.28374   , -0.30403966, -0.27414394, -0.10419572,
       -0.171618  , -0.3118946 ,  0.31250184, -0.31010735, -0.27575617,
       -0.30566223, -0.31215443, -0.25846428,  0.03596999, -0.03277335,
       -0.05168457, -0.05801276, -0.08948572])), (3.298696269122248, array([-0.10302201,  0.15576752, -0.03627567, -0.1885796 , -0.23549381,
       -0.10020513,  0.06259283, -0.03492182,  0.06869454,  0.14107999,
        0.06135972,  0.04944968,  0.23571734,  0.49504436, -0.02162802,
       -0.08908375, -0.50876505, -0.50882121])), (1.1973992330834702, array([-0.01682128, -0.2103712 ,  0.09114676,  0.00292723, -0.17134734,
       -0.10786554,  0.11710531, -0.10491826,  0.11525974, -0.2060556 ,
        0.11908566,  0.11051917, -0.221618  ,  0.04700176, -0.49301664,
        0.70478214, -0.07992261, -0.042357  ])), (1.1718387078485022, array([ 2.41878917e-01, -5.11666331e-03,  7.30756307e-02, -2.50161489e-01,
       -6.21822552e-01, -1.97812492e-01,  3.77814890e-02, -2.80012452e-02,
        5.36072098e-02,  1.77903514e-02, -5.41714358e-02,  3.08405978e-02,
        6.86194888e-05, -1.89384724e-01,  5.81746175e-01,  2.35988151e-01,
        7.44552697e-02,  5.42024985e-02])), (0.8599169977079716, array([-0.14809212,  0.08275834,  0.08155735, -0.086632  ,  0.00614117,
        0.77747049, -0.11190404,  0.1642555 , -0.09512993,  0.19809138,
       -0.11192548, -0.13994802,  0.00231352,  0.01556671,  0.16968933,
        0.38693895, -0.2193112 ,  0.07614891])), (0.7841154723760503, array([-0.05942021, -0.10353714, -0.07433939,  0.24967111,  0.50917202,
       -0.222541  , -0.00212654, -0.00870691, -0.0133934 , -0.18985456,
        0.07190079,  0.00223898,  0.00593323,  0.27602243,  0.57647175,
        0.37461919, -0.02893076, -0.13381953])), (0.3726295376446502, array([ 0.16122904, -0.40907045,  0.11529774,  0.09858287, -0.12846959,
        0.39577036,  0.11736031, -0.10562388,  0.11270669, -0.36893805,
        0.20621253,  0.11370464, -0.41392483,  0.12621942,  0.17106465,
       -0.35902178, -0.10906093, -0.15490759])), (0.2568950194548444, array([-0.82724913, -0.02666922,  0.18330636, -0.04628288, -0.05574171,
       -0.03607892,  0.1584799 , -0.15352677,  0.12363147, -0.17573803,
        0.05834921,  0.1645534 ,  0.10952953, -0.32142624,  0.13668823,
       -0.08177925, -0.08179558,  0.04565625])), (0.1267972928557123, array([-0.25863551, -0.00833879, -0.37415878, -0.02132866, -0.25650263,
        0.15674246, -0.00059808, -0.06718824, -0.08731773,  0.04822722,
        0.37312866,  0.04517693,  0.09133313,  0.45739083,  0.0122038 ,
        0.02850618,  0.55001091,  0.15960631])), (0.09180915364321345, array([-0.11112005,  0.07159399, -0.27683387, -0.23538919,  0.16165698,
       -0.07585635,  0.19955312, -0.16571795,  0.2571496 ,  0.43070289,
       -0.23047502,  0.19378755, -0.60462453,  0.10328553,  0.07470466,
       -0.02441504, -0.05822588,  0.15232009])), (0.06843686227401986, array([-0.14318658,  0.04643655,  0.75096561, -0.01871818, -0.01447591,
       -0.17949933, -0.15331492,  0.12470425, -0.18178712,  0.24103735,
        0.08539383, -0.11503619, -0.30647834,  0.303377  ,  0.01375136,
       -0.05306956,  0.20186967,  0.01268617])), (0.05461986976927837, array([-0.0828628 ,  0.20019452, -0.16709712,  0.28143598, -0.07943558,
        0.03452673, -0.05479317,  0.05603121, -0.09052048,  0.22743986,
        0.22964435, -0.07799652, -0.29373764, -0.39319444,  0.018703  ,
        0.06657886,  0.19928781, -0.65433912])), (0.037033767660817486, array([-0.0726179 ,  0.10724336, -0.14959664,  0.57106977, -0.27821877,
       -0.15992551, -0.07964294,  0.10786756, -0.10942836,  0.09982277,
        0.22281907, -0.10900343, -0.17141906,  0.02011335,  0.03294455,
       -0.04793937, -0.4623318 ,  0.42822063])), (0.0207847620703778, array([-0.09622428, -0.51626269,  0.01898898,  0.33322286, -0.12902326,
       -0.01166699, -0.11469443,  0.16135677,  0.52749911,  0.33662114,
       -0.27522586, -0.14116456,  0.20003726,  0.05846814, -0.01294677,
       -0.00989682,  0.13320412, -0.09029721])), (0.01882310874197125, array([-0.04812049,  0.57284054,  0.03610536,  0.22879799, -0.14716102,
        0.07316285, -0.07821108,  0.04407891,  0.37834608, -0.43872853,
       -0.38811808, -0.07791286, -0.13730499,  0.19530921, -0.0146432 ,
       -0.00184353,  0.17379716, -0.03256948])), (0.013266176739548647, array([ 0.03017785,  0.1037472 , -0.02786872, -0.34570645,  0.15641945,
       -0.05117524, -0.2901086 ,  0.24869847,  0.54528818, -0.06552166,
        0.54060673, -0.26733579, -0.05236284, -0.10433784,  0.01219561,
       -0.0103511 , -0.08298814,  0.09385527])), (0.004410590045403887, array([ 0.00725499, -0.01056858, -0.01134463,  0.02337208, -0.01105698,
        0.01481233, -0.76438461, -0.14265648,  0.00566774,  0.01746646,
       -0.02711643,  0.62487892, -0.00222617, -0.02212909,  0.00254272,
        0.01141135, -0.0427211 , -0.01348153])), (0.0027791009835487334, array([-4.75830215e-03, -2.91190097e-02,  8.49468526e-03, -2.56958203e-03,
       -3.57653719e-03,  1.35208987e-02, -2.68058316e-01, -8.09360912e-01,
        1.24450218e-03,  3.12646668e-02, -2.58420584e-02, -5.18284330e-01,
       -5.07417276e-03, -5.10454494e-03,  3.46209019e-03,  6.63531149e-03,
       -3.95143520e-02,  1.96927259e-04]))]
Eigenvalues in descending order: 
[9.64104985312631, 3.298696269122248, 1.1973992330834702, 1.1718387078485022, 0.8599169977079716, 0.7841154723760503, 0.3726295376446502, 0.2568950194548444, 0.1267972928557123, 0.09180915364321345, 0.06843686227401986, 0.05461986976927837, 0.037033767660817486, 0.0207847620703778, 0.01882310874197125, 0.013266176739548647, 0.004410590045403887, 0.0027791009835487334]

tot = sum(eigenvalues)
var_explained = [(i / tot) for i in sorted(eigenvalues, reverse=True)] 
cum_var_exp = np.cumsum(var_explained)

plt.bar(range(1,19), var_explained, alpha=0.5, align='center', label='individual explained variance')
plt.step(range(1,19),cum_var_exp, where= 'mid', label='cumulative explained variance')
plt.ylabel('Explained variance ratio')
plt.xlabel('Principal components')
plt.legend(loc = 'best')
plt.show()

pca = PCA(n_components=9) # reducing the dimension to 9 from 19
X_pca = pca.fit_transform(X_std)

from sklearn import model_selection

X_train, X_test, y_train, y_test = model_selection.train_test_split(X_pca, y1, test_size=0.30, random_state=1)

clf.fit(X_train, y_train)

SVC()

clf.score(X_test, y_test)

0.9330708661417323

y_pred = clf.predict(X_test)
accuracy_score(y_test, y_pred)

0.9330708661417323

pd.crosstab(y_test, y_pred)

print(metrics.classification_report(y_test, y_pred))

              precision    recall  f1-score   support

         bus       0.91      0.98      0.94        59
         car       0.96      0.95      0.95       133
         van       0.90      0.85      0.88        62

    accuracy                           0.93       254
   macro avg       0.92      0.93      0.92       254
weighted avg       0.93      0.93      0.93       254

--- End----

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio	class
0	95	48.0	83.0	178.0	72.0	10	162.0	42.0	20.0	159	176.0	379.0	184.0	70.0	6.0	16.0	187.0	197	van
1	91	41.0	84.0	141.0	57.0	9	149.0	45.0	19.0	143	170.0	330.0	158.0	72.0	9.0	14.0	189.0	199	van
2	104	50.0	106.0	209.0	66.0	10	207.0	32.0	23.0	158	223.0	635.0	220.0	73.0	14.0	9.0	188.0	196	car
3	93	41.0	82.0	159.0	63.0	9	144.0	46.0	19.0	143	160.0	309.0	127.0	63.0	6.0	10.0	199.0	207	van
4	85	44.0	70.0	205.0	103.0	52	149.0	45.0	19.0	144	241.0	325.0	188.0	127.0	9.0	11.0	180.0	183	bus
5	107	NaN	106.0	172.0	50.0	6	255.0	26.0	28.0	169	280.0	957.0	264.0	85.0	5.0	9.0	181.0	183	bus
6	97	43.0	73.0	173.0	65.0	6	153.0	42.0	19.0	143	176.0	361.0	172.0	66.0	13.0	1.0	200.0	204	bus
7	90	43.0	66.0	157.0	65.0	9	137.0	48.0	18.0	146	162.0	281.0	164.0	67.0	3.0	3.0	193.0	202	van
8	86	34.0	62.0	140.0	61.0	7	122.0	54.0	17.0	127	141.0	223.0	112.0	64.0	2.0	14.0	200.0	208	van
9	93	44.0	98.0	NaN	62.0	11	183.0	36.0	22.0	146	202.0	505.0	152.0	64.0	4.0	14.0	195.0	204	car

	count	mean	std	min	25%	50%	75%	max
compactness	846.0	93.678487	8.234474	73.0	87.00	93.0	100.0	119.0
circularity	841.0	44.828775	6.152172	33.0	40.00	44.0	49.0	59.0
distance_circularity	842.0	82.110451	15.778292	40.0	70.00	80.0	98.0	112.0
radius_ratio	840.0	168.888095	33.520198	104.0	141.00	167.0	195.0	333.0
pr.axis_aspect_ratio	844.0	61.678910	7.891463	47.0	57.00	61.0	65.0	138.0
max.length_aspect_ratio	846.0	8.567376	4.601217	2.0	7.00	8.0	10.0	55.0
scatter_ratio	845.0	168.901775	33.214848	112.0	147.00	157.0	198.0	265.0
elongatedness	845.0	40.933728	7.816186	26.0	33.00	43.0	46.0	61.0
pr.axis_rectangularity	843.0	20.582444	2.592933	17.0	19.00	20.0	23.0	29.0
max.length_rectangularity	846.0	147.998818	14.515652	118.0	137.00	146.0	159.0	188.0
scaled_variance	843.0	188.631079	31.411004	130.0	167.00	179.0	217.0	320.0
scaled_variance.1	844.0	439.494076	176.666903	184.0	318.00	363.5	587.0	1018.0
scaled_radius_of_gyration	844.0	174.709716	32.584808	109.0	149.00	173.5	198.0	268.0
scaled_radius_of_gyration.1	842.0	72.447743	7.486190	59.0	67.00	71.5	75.0	135.0
skewness_about	840.0	6.364286	4.920649	0.0	2.00	6.0	9.0	22.0
skewness_about.1	845.0	12.602367	8.936081	0.0	5.00	11.0	19.0	41.0
skewness_about.2	845.0	188.919527	6.155809	176.0	184.00	188.0	193.0	206.0
hollows_ratio	846.0	195.632388	7.438797	181.0	190.25	197.0	201.0	211.0

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio
0	95	48.0	83.0	178.0	72.0	10	162.0	42.0	20.0	159	176.0	379.0	184.0	70.0	6.0	16.0	187.0	197
1	91	41.0	84.0	141.0	57.0	9	149.0	45.0	19.0	143	170.0	330.0	158.0	72.0	9.0	14.0	189.0	199
2	104	50.0	106.0	209.0	66.0	10	207.0	32.0	23.0	158	223.0	635.0	220.0	73.0	14.0	9.0	188.0	196
3	93	41.0	82.0	159.0	63.0	9	144.0	46.0	19.0	143	160.0	309.0	127.0	63.0	6.0	10.0	199.0	207
4	85	44.0	70.0	205.0	103.0	52	149.0	45.0	19.0	144	241.0	325.0	188.0	127.0	9.0	11.0	180.0	183

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio
0	0.250889	0.580135	0.097253	0.349942	1.460002	0.747156	-0.041313	0.163051	-0.019052	0.792459	-0.293757	-0.118211	0.349953	-0.209612	0.148898	0.535243	-0.261662	0.133806
1	-0.247957	-0.576832	0.160094	-0.816888	-0.648789	0.435588	-0.511247	0.539220	-0.525853	-0.284310	-0.522119	-0.504364	-0.461911	0.117730	0.681443	0.342020	0.071166	0.414230
2	1.227603	0.873652	1.471555	1.180118	0.793256	0.747156	1.155070	-1.152131	1.045385	0.729773	1.114092	1.149019	1.341615	0.268943	1.379928	-0.215881	-0.093673	-0.003447
3	0.007051	-0.576832	0.034093	-0.220848	0.383072	0.435588	-0.712978	0.662983	-0.525853	-0.284310	-0.935944	-0.695261	-1.574168	-1.686670	0.148898	-0.093508	1.561779	1.617513
4	-1.088077	-0.053863	-0.750785	1.079043	3.209391	3.973288	-0.511247	0.539220	-0.525853	-0.212040	1.523408	-0.548251	0.467024	3.312080	0.681443	0.022763	-1.532626	-1.616862

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio	class
0	0.250889	0.580135	0.097253	0.349942	1.460002	0.747156	-0.041313	0.163051	-0.019052	0.792459	-0.293757	-0.118211	0.349953	-0.209612	0.148898	0.535243	-0.261662	0.133806	van
1	-0.247957	-0.576832	0.160094	-0.816888	-0.648789	0.435588	-0.511247	0.539220	-0.525853	-0.284310	-0.522119	-0.504364	-0.461911	0.117730	0.681443	0.342020	0.071166	0.414230	van
2	1.227603	0.873652	1.471555	1.180118	0.793256	0.747156	1.155070	-1.152131	1.045385	0.729773	1.114092	1.149019	1.341615	0.268943	1.379928	-0.215881	-0.093673	-0.003447	car
3	0.007051	-0.576832	0.034093	-0.220848	0.383072	0.435588	-0.712978	0.662983	-0.525853	-0.284310	-0.935944	-0.695261	-1.574168	-1.686670	0.148898	-0.093508	1.561779	1.617513	van
4	-1.088077	-0.053863	-0.750785	1.079043	3.209391	3.973288	-0.511247	0.539220	-0.525853	-0.212040	1.523408	-0.548251	0.467024	3.312080	0.681443	0.022763	-1.532626	-1.616862	bus

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio
compactness	1.000000	0.648513	0.777662	0.707969	0.161585	0.359017	0.771602	-0.784543	0.776425	0.654525	0.741832	0.775228	0.543655	-0.271115	0.197084	0.143729	0.364105	0.390185
circularity	0.648513	1.000000	0.780452	0.614104	0.189088	0.458461	0.827850	-0.816120	0.828516	0.962441	0.792711	0.821620	0.932274	0.105080	0.138196	-0.030641	-0.078661	0.029405
distance_circularity	0.777662	0.780452	1.000000	0.786261	0.233360	0.554384	0.914529	-0.911667	0.911610	0.766775	0.884154	0.913681	0.690531	-0.205292	0.084505	0.241224	0.192830	0.324180
radius_ratio	0.707969	0.614104	0.786261	1.000000	0.685795	0.485509	0.777654	-0.804529	0.761803	0.567679	0.820374	0.790084	0.525666	-0.293409	0.031756	0.157922	0.449663	0.492371
pr.axis_aspect_ratio	0.161585	0.189088	0.233360	0.685795	1.000000	0.308149	0.207715	-0.241712	0.189232	0.144730	0.275515	0.225100	0.133556	-0.196320	-0.051683	-0.026461	0.392798	0.403622
max.length_aspect_ratio	0.359017	0.458461	0.554384	0.485509	0.308149	1.000000	0.413294	-0.390265	0.416005	0.538460	0.430869	0.401119	0.326457	-0.197144	0.026354	0.112037	0.112418	0.357824
scatter_ratio	0.771602	0.827850	0.914529	0.777654	0.207715	0.413294	1.000000	-0.993486	0.986466	0.791645	0.959768	0.994745	0.773461	-0.017400	0.047472	0.189254	0.071914	0.151573
elongatedness	-0.784543	-0.816120	-0.911667	-0.804529	-0.241712	-0.390265	-0.993486	1.000000	-0.981512	-0.773461	-0.963479	-0.996271	-0.760251	0.055649	-0.046397	-0.172487	-0.134561	-0.195126
pr.axis_rectangularity	0.776425	0.828516	0.911610	0.761803	0.189232	0.416005	0.986466	-0.981512	1.000000	0.798061	0.944046	0.984235	0.772019	-0.013099	0.054186	0.192377	0.055813	0.142379
max.length_rectangularity	0.654525	0.962441	0.766775	0.567679	0.144730	0.538460	0.791645	-0.773461	0.798061	1.000000	0.748092	0.782676	0.871857	0.083790	0.127713	-0.010877	-0.070827	0.064959
scaled_variance	0.741832	0.792711	0.884154	0.820374	0.275515	0.430869	0.959768	-0.963479	0.944046	0.748092	1.000000	0.962269	0.756536	0.045770	0.016167	0.176640	0.086126	0.126152
scaled_variance.1	0.775228	0.821620	0.913681	0.790084	0.225100	0.401119	0.994745	-0.996271	0.984235	0.782676	0.962269	1.000000	0.766812	-0.034191	0.045544	0.178169	0.102986	0.172951
scaled_radius_of_gyration	0.543655	0.932274	0.690531	0.525666	0.133556	0.326457	0.773461	-0.760251	0.772019	0.871857	0.756536	0.766812	1.000000	0.254500	0.171176	-0.069807	-0.210354	-0.135990
scaled_radius_of_gyration.1	-0.271115	0.105080	-0.205292	-0.293409	-0.196320	-0.197144	-0.017400	0.055649	-0.013099	0.083790	0.045770	-0.034191	0.254500	1.000000	-0.078408	-0.101010	-0.846691	-0.889320
skewness_about	0.197084	0.138196	0.084505	0.031756	-0.051683	0.026354	0.047472	-0.046397	0.054186	0.127713	0.016167	0.045544	0.171176	-0.078408	1.000000	-0.032580	0.097731	0.070006
skewness_about.1	0.143729	-0.030641	0.241224	0.157922	-0.026461	0.112037	0.189254	-0.172487	0.192377	-0.010877	0.176640	0.178169	-0.069807	-0.101010	-0.032580	1.000000	0.069218	0.175997
skewness_about.2	0.364105	-0.078661	0.192830	0.449663	0.392798	0.112418	0.071914	-0.134561	0.055813	-0.070827	0.086126	0.102986	-0.210354	-0.846691	0.097731	0.069218	1.000000	0.901292
hollows_ratio	0.390185	0.029405	0.324180	0.492371	0.403622	0.357824	0.151573	-0.195126	0.142379	0.064959	0.126152	0.172951	-0.135990	-0.889320	0.070006	0.175997	0.901292	1.000000